{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# 语义分割和数据集\n",
    "\n",
    "最重要的语义分割数据集之一是[Pascal VOC2012](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "origin_pos": 4,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import os\n",
    "import torch\n",
    "import torchvision\n",
    "from d2l import torch as d2l\n",
    "\n",
    "d2l.DATA_HUB['voc2012'] = (d2l.DATA_URL + 'VOCtrainval_11-May-2012.tar',\n",
    "                           '4e443f8a2eca6b1dac8a6c57641b67dd40621a49')\n",
    "\n",
    "voc_dir = d2l.download_extract('voc2012', 'VOCdevkit/VOC2012')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "将所有输入的图像和标签读入内存"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "origin_pos": 7,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "def read_voc_images(voc_dir, is_train=True):\n",
    "    \"\"\"读取所有VOC图像并标注\"\"\"\n",
    "    txt_fname = os.path.join(voc_dir, 'ImageSets', 'Segmentation',\n",
    "                             'train.txt' if is_train else 'val.txt')\n",
    "    mode = torchvision.io.image.ImageReadMode.RGB\n",
    "    with open(txt_fname, 'r') as f:\n",
    "        images = f.read().split()\n",
    "    features, labels = [], []\n",
    "    for i, fname in enumerate(images):\n",
    "        features.append(torchvision.io.read_image(os.path.join(\n",
    "            voc_dir, 'JPEGImages', f'{fname}.jpg')))\n",
    "        labels.append(torchvision.io.read_image(os.path.join(\n",
    "            voc_dir, 'SegmentationClass' ,f'{fname}.png'), mode))\n",
    "    return features, labels\n",
    "\n",
    "train_features, train_labels = read_voc_images(voc_dir, True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "绘制前5个输入图像及其标签"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "origin_pos": 10,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x216 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 5\n",
    "imgs = train_features[0:n] + train_labels[0:n]\n",
    "imgs = [img.permute(1,2,0) for img in imgs]\n",
    "d2l.show_images(imgs, 2, n);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "列举RGB颜色值和类名"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "origin_pos": 12,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "VOC_COLORMAP = [[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0],\n",
    "                [0, 0, 128], [128, 0, 128], [0, 128, 128], [128, 128, 128],\n",
    "                [64, 0, 0], [192, 0, 0], [64, 128, 0], [192, 128, 0],\n",
    "                [64, 0, 128], [192, 0, 128], [64, 128, 128], [192, 128, 128],\n",
    "                [0, 64, 0], [128, 64, 0], [0, 192, 0], [128, 192, 0],\n",
    "                [0, 64, 128]]\n",
    "\n",
    "VOC_CLASSES = ['background', 'aeroplane', 'bicycle', 'bird', 'boat',\n",
    "               'bottle', 'bus', 'car', 'cat', 'chair', 'cow',\n",
    "               'diningtable', 'dog', 'horse', 'motorbike', 'person',\n",
    "               'potted plant', 'sheep', 'sofa', 'train', 'tv/monitor']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "查找标签中每个像素的类索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "origin_pos": 15,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "def voc_colormap2label():\n",
    "    \"\"\"构建从RGB到VOC类别索引的映射\"\"\"\n",
    "    colormap2label = torch.zeros(256 ** 3, dtype=torch.long)\n",
    "    for i, colormap in enumerate(VOC_COLORMAP):\n",
    "        colormap2label[\n",
    "            (colormap[0] * 256 + colormap[1]) * 256 + colormap[2]] = i\n",
    "    return colormap2label\n",
    "\n",
    "def voc_label_indices(colormap, colormap2label):\n",
    "    \"\"\"将VOC标签中的RGB值映射到它们的类别索引\"\"\"\n",
    "    colormap = colormap.permute(1, 2, 0).numpy().astype('int32')\n",
    "    idx = ((colormap[:, :, 0] * 256 + colormap[:, :, 1]) * 256\n",
    "           + colormap[:, :, 2])\n",
    "    return colormap2label[idx]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "例如"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "origin_pos": 17,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n",
       "         [0, 0, 0, 0, 0, 0, 0, 1, 1, 1],\n",
       "         [0, 0, 0, 0, 0, 0, 1, 1, 1, 1],\n",
       "         [0, 0, 0, 0, 0, 1, 1, 1, 1, 1],\n",
       "         [0, 0, 0, 0, 0, 1, 1, 1, 1, 1],\n",
       "         [0, 0, 0, 0, 1, 1, 1, 1, 1, 1],\n",
       "         [0, 0, 0, 0, 0, 1, 1, 1, 1, 1],\n",
       "         [0, 0, 0, 0, 0, 1, 1, 1, 1, 1],\n",
       "         [0, 0, 0, 0, 0, 0, 1, 1, 1, 1],\n",
       "         [0, 0, 0, 0, 0, 0, 0, 0, 1, 1]]),\n",
       " 'aeroplane')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = voc_label_indices(train_labels[0], voc_colormap2label())\n",
    "y[105:115, 130:140], VOC_CLASSES[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "使用图像增广中的随机裁剪，裁剪输入图像和标签的相同区域"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "origin_pos": 22,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x216 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def voc_rand_crop(feature, label, height, width):\n",
    "    \"\"\"随机裁剪特征和标签图像\"\"\"\n",
    "    rect = torchvision.transforms.RandomCrop.get_params(\n",
    "        feature, (height, width))\n",
    "    feature = torchvision.transforms.functional.crop(feature, *rect)\n",
    "    label = torchvision.transforms.functional.crop(label, *rect)\n",
    "    return feature, label\n",
    "\n",
    "imgs = []\n",
    "for _ in range(n):\n",
    "    imgs += voc_rand_crop(train_features[0], train_labels[0], 200, 300)\n",
    "\n",
    "imgs = [img.permute(1, 2, 0) for img in imgs]\n",
    "d2l.show_images(imgs[::2] + imgs[1::2], 2, n);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "自定义语义分割数据集类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "origin_pos": 25,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "class VOCSegDataset(torch.utils.data.Dataset):\n",
    "    \"\"\"一个用于加载VOC数据集的自定义数据集\"\"\"\n",
    "\n",
    "    def __init__(self, is_train, crop_size, voc_dir):\n",
    "        self.transform = torchvision.transforms.Normalize(\n",
    "            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
    "        self.crop_size = crop_size\n",
    "        features, labels = read_voc_images(voc_dir, is_train=is_train)\n",
    "        self.features = [self.normalize_image(feature)\n",
    "                         for feature in self.filter(features)]\n",
    "        self.labels = self.filter(labels)\n",
    "        self.colormap2label = voc_colormap2label()\n",
    "        print('read ' + str(len(self.features)) + ' examples')\n",
    "\n",
    "    def normalize_image(self, img):\n",
    "        return self.transform(img.float() / 255)\n",
    "\n",
    "    def filter(self, imgs):\n",
    "        return [img for img in imgs if (\n",
    "            img.shape[1] >= self.crop_size[0] and\n",
    "            img.shape[2] >= self.crop_size[1])]\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        feature, label = voc_rand_crop(self.features[idx], self.labels[idx],\n",
    "                                       *self.crop_size)\n",
    "        return (feature, voc_label_indices(label, self.colormap2label))\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "读取数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "origin_pos": 27,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "read 1114 examples\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "read 1078 examples\n"
     ]
    }
   ],
   "source": [
    "crop_size = (320, 480)\n",
    "voc_train = VOCSegDataset(True, crop_size, voc_dir)\n",
    "voc_test = VOCSegDataset(False, crop_size, voc_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "origin_pos": 30,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([64, 3, 320, 480])\n",
      "torch.Size([64, 320, 480])\n"
     ]
    }
   ],
   "source": [
    "batch_size = 64\n",
    "train_iter = torch.utils.data.DataLoader(voc_train, batch_size, shuffle=True,\n",
    "                                    drop_last=True,\n",
    "                                    num_workers=d2l.get_dataloader_workers())\n",
    "for X, Y in train_iter:\n",
    "    print(X.shape)\n",
    "    print(Y.shape)\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "整合所有组件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "origin_pos": 33,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "def load_data_voc(batch_size, crop_size):\n",
    "    \"\"\"加载VOC语义分割数据集\"\"\"\n",
    "    voc_dir = d2l.download_extract('voc2012', os.path.join(\n",
    "        'VOCdevkit', 'VOC2012'))\n",
    "    num_workers = d2l.get_dataloader_workers()\n",
    "    train_iter = torch.utils.data.DataLoader(\n",
    "        VOCSegDataset(True, crop_size, voc_dir), batch_size,\n",
    "        shuffle=True, drop_last=True, num_workers=num_workers)\n",
    "    test_iter = torch.utils.data.DataLoader(\n",
    "        VOCSegDataset(False, crop_size, voc_dir), batch_size,\n",
    "        drop_last=True, num_workers=num_workers)\n",
    "    return train_iter, test_iter"
   ]
  }
 ],
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   "name": "python"
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